We introduce real-time deformability cytometry (RT-DC) for continuous cell mechanical characterization of large populations (>100,000 cells) with analysis rates greater than 100 cells/s. RT-DC is sensitive to cytoskeletal alterations and can distinguish cell-cycle phases, track stem cell differentiation into distinct lineages and identify cell populations in whole blood by their mechanical fingerprints. This technique adds a new marker-free dimension to flow cytometry with diverse applications in biology, biotechnology and medicine.

In Escherichia coli, the pole-to-pole oscillation of the Min proteins directs septum formation to midcell, which is required for symmetric cell division. In vitro, protein waves emerge from the self-organization of MinD, a membrane-binding ATPase, and its activator MinE. For wave propagation, the proteins need to cycle through states of collective membrane binding and unbinding. Although MinD presumably undergoes cooperative membrane attachment, it is unclear how synchronous detachment is coordinated. We used confocal and single-molecule microscopy to elucidate the order of events during Min wave propagation. We propose that protein detachment at the rear of the wave, and the formation of the E-ring, are accomplished by two complementary processes: first, local accumulation of MinE due to rapid rebinding, leading to dynamic instability; and second, a structural change induced by membrane-interaction of MinE in an equimolar MinD-MinE (MinDE) complex, which supports the robustness of pattern formation.

Blood is arguably the most important bodily fluid and its analysis provides crucial health status information. A first routine measure to narrow down diagnosis in clinical practice is the differential blood count, determining the frequency of all major blood cells. What is lacking to advance initial blood diagnostics is an unbiased and quick functional assessment of blood that can narrow down the diagnosis and generate specific hypotheses. To address this need, we introduce the continuous, cell-by-cell morpho-rheological (MORE) analysis of diluted whole blood, without labeling, enrichment or separation, at rates of 1000 cells/sec. In a drop of blood we can identify all major blood cells and characterize their pathological changes in several disease conditions in vitro and in patient samples. This approach takes previous results of mechanical studies on specifically isolated blood cells to the level of application directly in blood and adds a functional dimension to conventional blood analysis.

We present a consistent theoretical approach for the study of nonequilibrium effects in chiral fluid dynamics within the framework of the linear sigma model with constituent quarks. Treating the quarks as an equilibrated heat bath we use the influence functional formalism to obtain a Langevin equation for the sigma field. This allows us to calculate the explicit form of the damping coefficient and the noise correlators. For a selfconsistent derivation of both the dynamics of the sigma field and the quark fluid we have to employ the 2PI (two-particle irreducible) effective action formalism. The energy dissipation from the field to the fluid is treated in the exact formalism of the 2PI effective action where a conserved energy-momentum tensor can be constructed. We derive its form and comment on approximations generating additional terms in the energy-momentum balance of the entire system

The throughput of cell mechanical characterization has recently approached that of conventional flow cytometers. However, this very sensitive, label-free approach still lacks the specificity of molecular markers. Here we developed an approach that combines real-time 1D-imaging fluorescence and deformability cytometry in one instrument (RT-FDC), thus opening many new research avenues. We demonstrated its utility by using subcellular fluorescence localization to identify mitotic cells and test for mechanical changes in those cells in an RNA interference screen.

Standardized polyacrylamide microgel beads as novel tools to calibrate experiments in biomechanics and to measure stresses in complex tissues.

We present a fully dynamical model to study nonequilibrium effects in both the chiral and the deconfinement phase transition. The sigma field and the Polyakov loop as the corresponding order parameters are propagated by Langevin equations of motion. The locally thermalized background is provided by a fluid of quarks and antiquarks. Allowing for an exchange of energy and momentum through dissipative and stochastic processes we ensure that the total energy of the coupled system remains conserved. We study its relaxational dynamics in different quench scenarios and are able to observe critical slowing down as well as the enhancement of long wavelength modes at the critical point. During the fluid dynamical expansion of a hot plasma fireball typical nonequilibrium effects like supercooling and domain formation occur when the system evolves through the first order phase transition.

The identification and separation of specific cells from heterogeneous populations is an essential prerequisite for further analysis or use. Conventional passive and active separation approaches rely on fluorescent or magnetic tags introduced to the cells of interest through molecular markers. Such labeling is time-and cost-intensive, can alter cellular properties, and might be incompatible with subsequent use, for example, in transplantation. Alternative label-free approaches utilizing morphological or mechanical features are attractive, but lack molecular specificity. Here we combine image-based real-time fluorescence and deformability cytometry (RT-FDC) with downstream cell sorting using standing surface acoustic waves (SSAW). We demonstrate basic sorting capabilities of the device by separating cell mimics and blood cell types based on fluorescence as well as deformability and other image parameters. The identification of blood sub-populations is enhanced by flow alignment and deformation of cells in the microfluidic channel constriction. In addition, the classification of blood cells using established fluorescence-based markers provides hundreds of thousands of labeled cell images used to train a deep neural network. The trained algorithm, with latency optimized to below 1 ms, is then used to identify and sort unlabeled blood cells at rates of 100 cells/sec. This approach transfers molecular specificity into labelfree sorting and opens up new possibilities for basic biological research and clinical therapeutic applications.